* feat(robots): consolidates bi SO setups
* fix(robots): solve circular dependecy
* fix(robots): teleop & record working
* feat(robots): only one SO
* fix(utils): rename bi so
* fix(scripts): bi so import
* fix(rl): remove imports
* Add basic support for PEFT adapter methods
This changes adds support for training policies with much less parameters
by applying adapter methods such as LoRA on specific parts of the policies
and therefore possibly higher learning rates / batch sizes.
To make this as accessible as possible I thought it useful to provide
defaults for `target_modules` and `modules_to_save`. Currently only SmolVLA
has such defaults but when we agree that this change is useful I will set
out to generate more such defaults. While the user can override these
settings, they are expected to only change the peft_method, rank and init_type
parameters.
* Implement loading of PEFT adapters
Loading a PEFT adapter is currently done by initializing a policy with default config
and then applying the adapter on the resulting model. This has the obvious drawback
that any configurations done during training are not applied in the adapted model.
Currently the `use_peft` attribute of `PreTrainedConfig` is only set during loading
to signal the following code that it has to deal with a PEFT adapter. However
we could imagine a scenario where this is already set at training time and stored
alongside the adapter.
* Store policy config alongside PEFT checkpoint
Before this change the PEFT-wrapped policy did not save the policy's config
alongside the adapter config / weights which prevented us from changing the
policy config. Now the policy config is saved both in full training and PEFT
training.
This change makes loading the PEFT policy adapter much easier as well.
* Add default config for ACT
* Support targets like `all-linear`
* Formatting
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix failing tests
* Remove PEFT compatibility changes in config
We'll wait for the PEFT release that fixes this for good.
* Remove `use_peft` parameter from training script
Instead we make the PEFT config optional which has the same effect.
* Log adapter config to WandB
* Better documentation for CLI arguments
* Don't unload & merge the PEFT model
This can make things hard when using quantized layers (user expects quantized base layers with
unquantized adapters for example, merging defaults to upcast the layers leading to higher
memory).
* Correct way of identifying when to save config
* Add CLI end-to-end tests
Currently there don't seem to be any way to test the CLI commands.
Since this change mostly happens in those I thought it best to add
a way to test these commands end-to-end.
More integrated commands like `lerobot-record` need patching but
standalone commands like training seem to work fine.
* Update default targets
Removed ACT since it doesn't make sense to fine-tune ACT without having it pretrained beforehand.
SmolVLA and Pi0/0.5 are much more senseful targets.
* Clean up loading code
- Centralized instantiation of the PEFT wrapper in `make_policy` for inference
(e.g. in `lerobot-record`)
- Training a PEFT policy also sets `cfg.use_peft` so that all inference code loading
the policy can rely on that attribute to identify if PEFT loading is needed
- Modified RTC example to also include PEFT policies. Mostly because this is an example
I'm currently exploring.
* Make sure push_to_hub works
Since PEFT only wraps `push_to_hub` and not `push_model_to_hub`, the reference
to `self` in `policy.push_model_to_hub` is the unwrapped policy which, of course,
doesn't know anything about PEFT.
To make the upload process aware of PEFT, we pass the unwrapped policy down to
`push_model_to_hub` as a kwarg. This is not ideal but I think it is the best way
for now.
* formatting
* Warn when encountering from-scratch-training
* Revamp pretrained model loading
There were quite a few factors that convinced me that the status quo
is able to load pretrained models from the PEFT adapter config but
in fact that didn't work.
This commit fixes the following things:
- policies wrapped in PEFT will now have a `name_or_path` attribute
containing the name or path of the pretrained model we're fine-tuning
- we further assume that SmolVLA without `pretrained_path` and
`load_vlm_weights==False` must be an user-side error
- we assume that using PEFT on from-scratch-policies must be
an user-side-error
* Make it possible to unset policy features
This is necessary to train pre-trained policies on new datasets so that the
features are inferred from the new dataset and not from the pretrained
policy.
* Use correct loading for PEFT in RTC example
* Make it possible to use PeftModels in eval
* Add test checking that PEFT actually reduces params
* Adapt state/action projections instead of full-finetuning
There doesn't seem to be a benefit to fully fine-tune these layers
over just adapting them, so we do that instead.
* Disallow PEFT training on non-pretrained policies
At first I thought it would make sense to have this feature
in case you want to fine-tune a pre-trained section but in the
end it makes more trouble than it's worth.
It's still possible to allow this in the future when a concrete
need arises.
* Add basic documentation
* Formatting
* Add peft as extra dependency, mark tests
Fast tests currently fail because of the missing dependency.
* Fix pre-commit issues
* Add walx <> peft conflict for uv
* Exclude peft from pi install for now
---------
Co-authored-by: nemo <git@ningu.net>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* support wallx
* fix bugs in flow
* incorporate wallx model into lerobot
* update the policy methods
* reduce to least config and params & pass lerobot basic test
* fixed dtype bugs
* add wallx dependencies
* update
* remove flash-attn requirement && fix bug in inference and fast mode
* fix bug for inference
* add some small modifications
* fix pre-commit errors
* remove lerobot[wallx]
* fix ci
* fix precommit issues
* fix: exclude wallx extra properly in CI workflows
* fix: add uv conflicts for wallx transformers version
* fix: peft test import
* pre-commit
* only export WallXConfig from wall_x package to avoid peft import in CI
* remove torch dep
* precommit
* add import
---------
Co-authored-by: vincentchen <chenlufang@x2robot.com>
Co-authored-by: Geoffrey19 <sympathischmann35@gmail.com>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Pepijn <pepijn@huggingface.co>
* add initial modeling
* make rewind pretrained policy
* add annotation
* small fix
* add sarm
* subtasks
* fix spawn
* fix rewind discrepancies
* Add script to generate embedding for dataset (#2138)
* Add generate and validate script
* fix precommit
* Improve generate embeddings function by using dataset tools (#2206)
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
* cleanup
* change order train log
* print batch size
* update sarm processor
* add reward output
* change expected features
* add image validation
* change validation
* get state input from dataset stats
* raise if no state key is found
* pass stats
* cleanup and refactor
* add episode inddex to complementary data
* add subtask init and detection
* revert lerobot_train changes
* pass dataset metadata to policy
* change loadig subtasks
* add small logging
* fix progress conversion and adding initial frame
* use large offset for initial frame (ugly)
* Remove rewind, use clip tokenizer
* add tests, implement formula 1,2 correctly and cleanup
* use task from dataset, cleanup visualizer
* simplify
* simplify and cleanup code and move compute_temporal_proportions to utils
* fix normalization in visualization
* Fix visualization and change prompt
* fix formatting
* add visualize subtask annotations
* use qwen thinking
* try different prompt
* format
* update prompt
* higher temp, long output
* different settings
* use instruct
* show full resp
* split message
* Temp: increase tolerance dataset
* Fix RA-BC (#2572)
* Add next observation loading for RA-BC progress deltas
* Compute weights based on temporal progress deltas instead of static rewards
* Add hard-masking for negative progress deltas in weight computation
* Feat/add dual head (#2582)
* Add dual dense sparse head and annotation
* Add docs
* add dual to procesor
* cleanup
* change sampling in visualize and cleanup
* remove validation
* remove compile
* Feat/test uniform (#2587)
* test uniform
* add different string for misaligned
* Fix rewind and add tests
* uncomment text implementation
* run precommit
* Add head mode for ra-bc
* fix visalization of single task
* add
* return per sample loss
* Fix RA_BC (#2602)
* update rabc implementation
* compute rabc beforehand
* fix import
* add only progress calulation
* use precomputed progress
* multi gpu processing
* import
* fix dataset meta data extraction
* add logging
* logging
* log
* progress per episode
* split differently
* move clip to gpu
* pre decode frames for an episode
* fix cuda initalization
* fix import
* multi processing
* rename
* fix import
* fix
* fix rabc
* use last known progress if oob
* use last known progress if oob
* add misalignment loss with random embeddings
* discard previous changes
* add selection of models to docs for ra_bc
* add transformers dep
* extend tolerance
* initial commit with new codebase
* add tests
* fix
* remove temporal sampler
* drop last frame for sampler
* use original ref
* some fixes
* fix visualization
* remove smoothing and fix order subtasks
* add stride rabc computation
* add push to hub
* add explanation
* add kappa expllaination
* better rabc logging
* feedback pr
* remove dataset tolerance
* revert dataset tool
* revert dataset changes
* add credit
* run precommit
* change path for generate ra_bc
* fix type
* include sarm in all in pyproject
* fix precommit
* lazy import matplotlib
* lazy import qwen
* remove rich console
* skip if transformers is not installed?
* run only when we have faker
* place transformer lazy loading
* Dont test if low transformer version
* fix
* increase transformer
* increase as 4.57.0 is yanked
* remove pi from all
* go back
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: s1lent4gnt <kmeftah.khalil@gmail.com>
* Add Real-Time Chunking (RTC) support for flow matching models
Implement Real-Time Chunking (RTC) for action chunking policies using flow
matching denoising. RTC enables smooth action transitions between consecutive
chunks by using prefix guidance during denoising.
Key features:
- RTCProcessor class with denoise_step method for RTC guidance
- Tracker system for debug tracking using time-based dictionary storage
- RTCDebugVisualizer with comprehensive visualization utilities
- Integration with SmolVLA policy for flow matching models
- Support for multiple prefix attention schedules (ZEROS, ONES, LINEAR, EXP)
- Configurable execution horizon and max guidance weight
- Example scripts for dataset evaluation and real-time control
Technical details:
- Uses autograd-based gradient computation for RTC corrections
- Time-based tracking eliminates duplicate step issues
- Proxy methods in RTCProcessor for cleaner API
- Full integration with LeRobot's policy and dataset systems
Files added/modified:
- src/lerobot/configs/types.py: Add RTCAttentionSchedule enum
- src/lerobot/policies/rtc/: Core RTC implementation
- configuration_rtc.py: RTC configuration
- modeling_rtc.py: RTCProcessor with denoise_step
- debug_handler.py: Tracker for debug information
- debug_visualizer.py: Visualization utilities
- src/lerobot/policies/smolvla/modeling_smolvla.py: RTC integration
- examples/rtc/: Example scripts and evaluation tools
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
Co-Authored-By: Claude <noreply@anthropic.com>
* Fix rtc_config attribute access in SmolVLA
Use getattr() to safely check for rtc_config attribute existence
instead of direct attribute access. This fixes AttributeError when
loading policies without rtc_config in their config.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
Co-Authored-By: Claude <noreply@anthropic.com>
* fixup! Fix rtc_config attribute access in SmolVLA
* Add RTCConfig field to SmolVLAConfig
Add rtc_config as an optional field in SmolVLAConfig to properly
support Real-Time Chunking configuration. This replaces the previous
getattr() workarounds with direct attribute access, making the code
cleaner and more maintainable.
Changes:
- Import RTCConfig in configuration_smolvla.py
- Add rtc_config: RTCConfig | None = None field
- Revert getattr() calls to direct attribute access in modeling_smolvla.py
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
Co-Authored-By: Claude <noreply@anthropic.com>
* Refactor RTC enabled checks to use _rtc_enabled helper
Add _rtc_enabled() helper method in VLAFlowMatching class to simplify
and clean up RTC enabled checks throughout the code. This reduces
code duplication and improves readability.
Changes:
- Add _rtc_enabled() method in VLAFlowMatching
- Replace verbose rtc_config checks with _rtc_enabled() calls
- Maintain exact same functionality with cleaner code
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
Co-Authored-By: Claude <noreply@anthropic.com>
* Rename track_debug method to track
Simplify the method name from track_debug to just track for better
readability and consistency. The method already has clear documentation
about its debug tracking purpose.
Changes:
- Rename RTCProcessor.track_debug() to track()
- Update all call sites in modeling_smolvla.py and modeling_rtc.py
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
Co-Authored-By: Claude <noreply@anthropic.com>
* Use output_dir for saving all evaluation images
Update eval_dataset.py to save all comparison images to the
configured output_dir instead of the current directory. This provides
better organization and allows users to specify where outputs should be
saved.
Changes:
- Add os import at top level
- Create output_dir at start of run_evaluation()
- Save all comparison images to output_dir
- Remove duplicate os imports
- Update init_rtc_processor() docstring to be more concise
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
Co-Authored-By: Claude <noreply@anthropic.com>
* fixup! Use output_dir for saving all evaluation images
* Fix logging buffering and enable tracking when RTC config provided
- Add force=True to logging.basicConfig to override existing configuration
- Enable line buffering for stdout/stderr for real-time log output
- Modify init_rtc_processor to create processor when rtc_config exists
even if RTC is disabled, allowing tracking of denoising data
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
* Refactor SmolVLA plotting to use tracker data instead of local variables
Remove local tracking variables (correction, x1_t, error) from the
denoising loop and instead retrieve plotting data from the RTC tracker
after each denoise step. This makes the code cleaner and uses the
tracker as the single source of truth for debug/visualization data.
Changes:
- Remove initialization of correction, x1_t, error before denoising loop
- After each Euler step, retrieve most recent debug step from tracker
- Extract correction, x1_t, err from debug step for plotting
- Update tracking condition to use is_debug_enabled() method
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
* Move plotting logic from modeling_smolvla to eval_dataset script
Refactor to improve separation of concerns:
modeling_smolvla.py changes:
- Remove all plotting logic from sample_actions method
- Remove viz_xt_axs, viz_vt_axs, viz_x1t_axs parameters
- Remove matplotlib and RTCDebugVisualizer imports
- Remove viz_fig, viz_axs, denoise_step_counter instance variables
- Simplify denoising loop to only track data in rtc_processor
eval_dataset.py changes:
- Add _plot_denoising_steps_from_tracker helper method
- Retrieve debug steps from tracker after inference
- Plot x_t, v_t, x1_t, correction, and error from tracker data
- Enable debug tracking (cfg.rtc.debug = True) for visualization
- Remove viz axes parameters from predict_action_chunk calls
modeling_rtc.py changes:
- Remove v_t from track() call (handled by user change)
Benefits:
- Cleaner modeling code focused on inference
- Evaluation script owns all visualization logic
- Better separation of concerns
- Tracker is single source of truth for debug data
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
* Refactor plotting loging
* fixup! Refactor plotting loging
* Improve visualization: separate correction plot and fix axis scaling
Changes:
- Create separate figure for correction data instead of overlaying on v_t
- Add _rescale_axes helper method to properly scale all axes
- Add 10% margin to y-axis for better visualization
- Fix v_t chart vertical compression issue
Benefits:
- Clearer v_t plot without correction overlay
- Better axis scaling with proper margins
- Separate correction figure for focused analysis
- Improved readability of all denoising visualizations
Output files:
- denoising_xt_comparison.png (x_t trajectories)
- denoising_vt_comparison.png (v_t velocity - now cleaner)
- denoising_correction_comparison.png (NEW - separate corrections)
- denoising_x1t_comparison.png (x1_t state with error)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
* fixup! Improve visualization: separate correction plot and fix axis scaling
* fixup! fixup! Improve visualization: separate correction plot and fix axis scaling
* fixup! fixup! fixup! Improve visualization: separate correction plot and fix axis scaling
* Fix traacking
* Right kwargs for the policy
* Add tests for tracker
* Fix tests
* Drop not required methods
* Add torch compilation for eval_dataset
* delete policies
* Add matplotliv to dev
* fixup! Add matplotliv to dev
* Experiemnt with late detach
* Debug
* Fix compilation
* Add RTC to PI0
* Pi0
* Pi0 eval dataset
* fixup! Pi0 eval dataset
* Turn off compilation for pi0/pi05
* fixup! Turn off compilation for pi0/pi05
* fixup! fixup! Turn off compilation for pi0/pi05
* fixup! fixup! fixup! Turn off compilation for pi0/pi05
* fixup! fixup! fixup! fixup! Turn off compilation for pi0/pi05
* fixup! fixup! fixup! fixup! fixup! Turn off compilation for pi0/pi05
* Add workable flow
* Small fixes
* Add more tests
* Add validatio at the end
* Update README
* Silent validation
* Fix tests
* Add tests for modeling_rtc
* Add tests for flow matching models with RTC
* fixup! Add tests for flow matching models with RTC
* fixup! fixup! Add tests for flow matching models with RTC
* Add one more test
* fixup! Add one more test
* Fix test to use _rtc_enabled() instead of is_rtc_enabled()
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* fixup! Fix test to use _rtc_enabled() instead of is_rtc_enabled()
* fixup! fixup! Fix test to use _rtc_enabled() instead of is_rtc_enabled()
* Add RTC initialization tests without config for PI0.5 and SmolVLA
Add test_pi05_rtc_initialization_without_rtc_config and
test_smolvla_rtc_initialization_without_rtc_config to verify that
policies can initialize without RTC config and that _rtc_enabled()
returns False in this case.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Fix PI0.5 init_rtc_processor to use getattr instead of direct model access
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Fix SmolVLA init_rtc_processor to use getattr instead of direct model access
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Fix PI0.5 RTC tests to use quantile stats (q01, q99) for normalization
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* fixup! Fix PI0.5 RTC tests to use quantile stats (q01, q99) for normalization
* Fixup eval with real robot
* fixup! Fixup eval with real robot
* fixup! fixup! Fixup eval with real robot
* Extract simulator logic from eval_with real robot and add proper headers to files
* Update images
* Fix tests
* fixup! Fix tests
* add docs for rtc
* enhance doc and add images
* Fix instal instructions
---------
Co-authored-by: Ben Zhang <benzhangniu@gmail.com>
Co-authored-by: Alexander Soare <alexander.soare159@gmail.com>
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
* add env from the hub support
* add safe loading
* changes
* add tests, docs
* more
* style/cleaning
* order
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
* Enhance OpenCVCamera with FOURCC support and validation
- Added FOURCC configuration option to OpenCVCamera and OpenCVCameraConfig for specifying video format.
- Implemented _validate_fourcc method to validate and set the camera's FOURCC code.
- Updated _configure_capture_settings to apply FOURCC settings before FPS and resolution.
- Enhanced camera detection to include default FOURCC code in camera info.
- Updated documentation to reflect new FOURCC parameter and its implications on performance.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add tests for FOURCC configuration in OpenCVCamera
- Implemented tests to validate FOURCC configuration and its application in OpenCVCamera.
- Added checks for valid FOURCC codes and ensured that invalid codes raise appropriate errors.
- Included a test for camera connection functionality using specified FOURCC settings.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix circular import in __init__.py - change to relative import
* Update src/lerobot/cameras/opencv/configuration_opencv.py
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Signed-off-by: hls <56255627+forgetwhatuwant@users.noreply.github.com>
* Update src/lerobot/cameras/opencv/configuration_opencv.py
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Signed-off-by: hls <56255627+forgetwhatuwant@users.noreply.github.com>
* fix(camera_opencv): ensure MSMF hardware transform compatibility on Windows before importing OpenCV
* This change reverts the import from a relative import (.) back to the absolute import (lerobot.) as it was previously
* opencv/config: satisfy Ruff SIM102 by merging nested if for fourcc validation
* style(opencv/config): apply ruff-format changes
---------
Signed-off-by: hls <56255627+forgetwhatuwant@users.noreply.github.com>
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: forgetwhatuwant <forgetwhatuwant@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* refactor(env): introduce explicit gym ID handling in EnvConfig/factory
This commit introduces properties for the gym package/ID associated
with and environment config. They default to the current defaults
(`gym_{package_name}/{task_id}`) to avoid breaking changes, but allow
for easier use of external gym environments.
Subclasses of `EnvConfig` can override the default properties to allow
the factory to import (i.e. register) the gym env from a specific module,
and also instantiate the env from any ID string.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* more changes
* quality
* fix test
---------
Co-authored-by: Ben Sprenger <ben.sprenger@rogers.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
* Enhance training and logging functionality with accelerator support
- Added support for multi-GPU training by introducing an `accelerator` parameter in training functions.
- Updated `update_policy` to handle gradient updates based on the presence of an accelerator.
- Modified logging to prevent duplicate messages in non-main processes.
- Enhanced `set_seed` and `get_safe_torch_device` functions to accommodate accelerator usage.
- Updated `MetricsTracker` to account for the number of processes when calculating metrics.
- Introduced a new feature in `pyproject.toml` for the `accelerate` library dependency.
* Initialize logging in training script for both main and non-main processes
- Added `init_logging` calls to ensure proper logging setup when using the accelerator and in standard training mode.
- This change enhances the clarity and consistency of logging during training sessions.
* add docs and only push model once
* Place logging under accelerate and update docs
* fix pre commit
* only log in main process
* main logging
* try with local rank
* add tests
* change runner
* fix test
* dont push to hub in multi gpu tests
* pre download dataset in tests
* small fixes
* fix path optimizer state
* update docs, and small improvements in train
* simplify accelerate main process detection
* small improvements in train
* fix OOM bug
* change accelerate detection
* add some debugging
* always use accelerate
* cleanup update method
* cleanup
* fix bug
* scale lr decay if we reduce steps
* cleanup logging
* fix formatting
* encorperate feedback pr
* add min memory to cpu tests
* use accelerate to determin logging
* fix precommit and fix tests
* chore: minor details
---------
Co-authored-by: AdilZouitine <adilzouitinegm@gmail.com>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
* make add_feature take multiple features at a time and rename to add_features
* - New function: modify_features that was a combination of remove features and add features.
- This function is important for when we want to add a feature and remove another so we can do it in one time to avoid copying and creating the dataset multiple times
* incremental parquet writing
* add .finalise() and a backup __del__ for stopping writers
* fix missing import
* precommit fixes added back the use of embed images
* added lazy loading for hf_Dataset to avoid frequently reloading the dataset during recording
* fix bug in video timestamps
* Added proper closing of parquet file before reading
* Added rigorous testing to validate the consistency of the meta data after creation of a new dataset
* fix bug in episode index during clear_episode_buffer
* fix(empty concat): check for empty paths list before data files concatenation
* fix(v3.0 message): updating v3.0 backward compatibility message.
* added fixes for the resume logic
* answering co-pilot review
* reverting some changes and style nits
* removed unused functions
* fix chunk_id and file_id when resuming
* - fix parquet loading when resuming
- add test to verify the parquet file integrity when resuming so that data files are now overwritten
* added general function get_file_size_in_mb and removed the one for video
* fix table size value when resuming
* Remove unnecessary reloading of the parquet file when resuming record.
Write to a new parquet file when resuming record
* added back reading parquet file for image datasets only
* - respond to Qlhoest comments
- Use pyarrows `from_pydict` function
- Add buffer for episode metadata to write to the parquet file in batches to improve efficiency
- Remove the use of `to_parquet_with_hf_images`
* fix(dataset_tools) with the new logic using proper finalize
bug in finding the latest path of the metdata that was pointing to the data files
added check for the metadata size in the case the metadatabuffer was not written yet
* nit in flush_metadata_buffer
* fix(lerobot_dataset) return the right dataset len when a subset of the dataset is requested
---------
Co-authored-by: Harsimrat Sandhawalia <hs.sandhawalia@gmail.com>
* feat(dataset-tools): add dataset utilities and example script
- Introduced dataset tools for LeRobotDataset, including functions for deleting episodes, splitting datasets, adding/removing features, and merging datasets.
- Added an example script demonstrating the usage of these utilities.
- Implemented comprehensive tests for all new functionalities to ensure reliability and correctness.
* style fixes
* move example to dataset dir
* missing lisence
* fixes mostly path
* clean comments
* move tests to functions instead of class based
* - fix video editting, decode, delete frames and rencode video
- copy unchanged video and parquet files to avoid recreating the entire dataset
* Fortify tooling tests
* Fix type issue resulting from saving numpy arrays with shape 3,1,1
* added lerobot_edit_dataset
* - revert changes in examples
- remove hardcoded split names
* update comment
* fix comment
add lerobot-edit-dataset shortcut
* Apply suggestion from @Copilot
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Signed-off-by: Michel Aractingi <michel.aractingi@huggingface.co>
* style nit after copilot review
* fix: bug in dataset root when editing the dataset in place (without setting new_repo_id
* Fix bug in aggregate.py when accumelating video timestamps; add tests to fortify aggregate videos
* Added missing output repo id
* migrate delete episode to using pyav instead of decoding, writing frames to disk and encoding again.
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
* added modified suffix in case repo_id is not set in delete_episode
* adding docs for dataset tools
* bump av version and add back time_base assignment
* linter
* modified push_to_hub logic in lerobot_edit_dataset
* fix(progress bar): fixing the progress bar issue in dataset tools
* chore(concatenate): removing no longer needed concatenate_datasets usage
* fix(file sizes forwarding): forwarding files and chunk sizes in metadata info when splitting and aggregating datasets
* style fix
* refactor(aggregate): Fix video indexing and timestamp bugs in dataset merging
There were three critical bugs in aggregate.py that prevented correct dataset merging:
1. Video file indices: Changed from += to = assignment to correctly reference
merged video files
2. Video timestamps: Implemented per-source-file offset tracking to maintain
continuous timestamps when merging split datasets (was causing non-monotonic
timestamp warnings)
3. File rotation offsets: Store timestamp offsets after rotation decision to
prevent out-of-bounds frame access (was causing "Invalid frame index" errors
with small file size limits)
Changes:
- Updated update_meta_data() to apply per-source-file timestamp offsets
- Updated aggregate_videos() to track offsets correctly during file rotation
- Added get_video_duration_in_s import for duration calculation
* Improved docs for split dataset and added a check for the possible case that the split size results in zero episodes
* chore(docs): update merge documentation details
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
---------
Co-authored-by: CarolinePascal <caroline8.pascal@gmail.com>
Co-authored-by: Jack Vial <vialjack@gmail.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* Add pre and post processing to async inference and update docs
* precommit fix typo
* fix tests
* refactor(async): no None branching for processors in _predict_action_chunk
---------
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
* initial commit
* change device in test
* do detailed import
* adhere to python 3.11 syntax
* fix autodocstring
* additionally
* do same in other files
* add model. prefix to all keys in state dict
* use dummy stats
* add pi05
* also shorten action_steps
* fix test
* all test pass! and fix tokenizer max length between 05 and 0
* remove test
* fix transformer dependency
* fix test
* split pi0 and pi05 policy in seperate files
* fix test
* fix push to hub test
* add some comments, license and readme
* remove warning in config
* add pi05 to factory
* remove check
* rename action_horizon to chunk_size
* clean up padding of state and action (more in line with lerobot pi0)
* add openpi image transforms for training and add more flexibility to _preprocess_images similar to lerobot pi0
* fix key match from pytorch state dict (similar keys to openpi implementation now)
* also for pi05
* update to python 3.11
* revert to openpi transformer replace python 3.11
* fix(modeling pi0): nit warning message
* use safeauto_docstring
* fix: remove unused param
* fix from pretrained
* add preprocess tests
* also compile forward method
* Do not add model prefix to normalization
* use same name for action and state dim as lerobot pi0 and remove fixed image keys
* load from pretrained_path
* temp: hardcode base model
* fix override self.pretrained_path = None overwrite
* rename to loss
* remove additional image augmentations, lerobot dataset already does this
* Add docs
* put tests in test folder
* Add test to instatiate all base models
* go back to python 3.10
* update docs
* adapt docs pi05
* change docs: finetune base model options
* minor docs fixes and dependencies
* remove todo
* cast float64 to float32 for mps
* skip if no transformers
* fix tests
* add new models to modelcard
* add back init
* fix circular input
* feat: only run pi test on GPU
* remove require_nightly_gpu
* replace decorator test_pi0_openpi
* rename action_dim, state_dim to max_action_dim, max_state_dim
* fix doc and constants
* cleanup tests
* fix from pretrained
* fix tests
* add comment pi0 pi05 tests, add image features to pi0 pi05 hub tests
* fix, state is included in language not in flow head
* Move test to specific folder
* and paligemma task with newline
* remove add_special_tokens, not needed
* feedback pr
* Remove previous pi0 and rename pi0_openpi and pi05_openpi
* Add Quantile stats to LeRobotDataset (#1985)
* - Add RunningQuantileStats class for efficient histogram-based quantile computation
- Integrate quantile parameters (compute_quantiles, quantiles) into LeRobotDataset
- Support quantile computation during episode collection and aggregation
- Add comprehensive function-based test suite (24 tests) for quantile functionality
- Maintain full backward compatibility with existing stats computation
- Enable configurable quantiles (default: [0.01, 0.99]) for robust normalization
* style fixes, make quantiles computation by default to new datasets
* fix tests
* - Added DEFAULT_QUANTILES=[0.01, 0.10, 0.50, 0.90, 0.99] to be computed for each features instead of being chosen by the user
- Fortified tests.
* - add helper functions to reshape stats
- add missing test for quantiles
* - Add QUANTILE normalization mode to normalize the data with the 1st and 99th percentiles.
- Add QUANTILE10 normalization mode to normalize the data with the 10th and 90th percentiles.
* style fixes
* Added missing lisence
* Simplify compute_stats
* - added script `augment_dataset_quantile_stats.py` so that we can add quantile stats to existing v3 datasets that dont have quatniles
- modified quantile computation instead of using the edge for the value, interpolate the values in the bin
* rename pi0/pi05 files
* Remove open pi patch and use custom transformer branch for now
* renaming
* fix
* Revert "fix"
This reverts commit 1ea65730ac.
* fix naming
* feet(pi0/pi0.5): add pipeline (#2009)
* feat(processor): convert openpi model with processor
* TODO: Make test works
* fix(modeling_pi0openpi): update attention mask value and time scaling; improve task handling in tests
- Changed the attention mask value from `self.config.attention_mask_value` to a fixed value of `-2.3819763e38`.
- Updated time scaling in the `sample_noise` method to use a constant factor of `0.999` and an offset of `0.001`.
- Enhanced task handling in tests to ensure proper formatting and batch size consistency.
- Cleaned up commented-out test code for clarity.
* refactor(pi0): rename PI0OpenPIConfig and PI0OpenPIPolicy to PI0Config and PI0Policy
- Updated imports and references throughout the codebase to reflect the new naming convention.
- Introduced a new processor file for PI0 to handle pre-processing and post-processing steps.
- Adjusted tests to utilize the renamed classes, ensuring consistency and functionality.
- Enhanced clarity and maintainability by removing outdated naming conventions.
* refactor(pi05): rename PI0OpenPIPolicy to PI0Policy and update configuration
- Renamed `PI0OpenPIPolicy` to `PI0Policy` for consistency with naming conventions.
- Updated the `PI05OpenPIConfig` to include a new `tokenizer_max_length` attribute and changed the normalization mode for state from `MEAN_STD` to `QUANTILES`.
- Simplified model initialization in `PI05OpenPIPolicy` by removing unused `dataset_stats` parameter.
- Added a new processor class for `Pi05PrepareStateTokenizerProcessorStep` with `@dataclass` for improved readability.
- Introduced a test script to compare the integration of the PI0OpenPI policy with the original implementation, ensuring local testing compatibility.
* feat(processor): convert openpi model with processor
* TODO: Make test works
* fix(modeling_pi0openpi): update attention mask value and time scaling; improve task handling in tests
- Changed the attention mask value from `self.config.attention_mask_value` to a fixed value of `-2.3819763e38`.
- Updated time scaling in the `sample_noise` method to use a constant factor of `0.999` and an offset of `0.001`.
- Enhanced task handling in tests to ensure proper formatting and batch size consistency.
- Cleaned up commented-out test code for clarity.
* refactor(pi0): rename PI0OpenPIConfig and PI0OpenPIPolicy to PI0Config and PI0Policy
- Updated imports and references throughout the codebase to reflect the new naming convention.
- Introduced a new processor file for PI0 to handle pre-processing and post-processing steps.
- Adjusted tests to utilize the renamed classes, ensuring consistency and functionality.
- Enhanced clarity and maintainability by removing outdated naming conventions.
* refactor(pi05): rename PI0OpenPIPolicy to PI0Policy and update configuration
- Renamed `PI0OpenPIPolicy` to `PI0Policy` for consistency with naming conventions.
- Updated the `PI05OpenPIConfig` to include a new `tokenizer_max_length` attribute and changed the normalization mode for state from `MEAN_STD` to `QUANTILES`.
- Simplified model initialization in `PI05OpenPIPolicy` by removing unused `dataset_stats` parameter.
- Added a new processor class for `Pi05PrepareStateTokenizerProcessorStep` with `@dataclass` for improved readability.
- Introduced a test script to compare the integration of the PI0OpenPI policy with the original implementation, ensuring local testing compatibility.
* refactor(pi05): update imports and rename configuration classes
- Changed imports to reflect the new naming convention for PI05 configuration and policy classes.
- Renamed `PI05OpenPIConfig` to `PI05Config` and `PI05OpenPIPolicy` to `PI05Policy` for consistency.
- Introduced a new processor file for PI05, implementing pre-processing and post-processing steps.
- Updated tests to utilize the renamed classes, ensuring functionality and consistency across the codebase.
* update(pi05): increase tokenizer_max_length for improved processing
- Changed the `tokenizer_max_length` from 48 to 200 to enhance the model's capability in handling longer sequences.
- This adjustment aims to improve the overall performance and flexibility of the PI05 configuration.
* add default for state (max_state_dim)
* correct naming
* fix import
* cleanup code
* remove unused test
* us quantiles for action
* move to device
* remove discrete state assert
* fix pi05 test
* move pi05 to device
* use base models in comparison tests
* small renames for tests
* change number of tokens pi05 test
* fix openpi tokenization in test
* fix hub test
* fix test
* assert lerobot vs openpi tests
---------
Co-authored-by: Pepijn <pepijn@huggingface.co>
* add headers
* add back previously removed imports
* update if statement load processor with dataset stats
* remove to avoid circular import
* inject dataset stats for pretrained models
* check normalization before applying
* add link to quantile augument script
* fix(policies): transformers import for ci in PI0 & PI05 (#2039)
* fix(policies): transformers import for ci in PI0
* fix(policies): transformers import for ci in PI05
* test(processor): fix expected raise when normalization types are missing (#2040)
* switch normalization order pipeline for pi05
* Fix/quantiles script (#2064)
* refactor augment stats with quantiles script
add parallelization for faster processing
shift the quantile normalization between -1 1
* fix replay buffer tests
* fix comment
* overwrite the pipeline normalization features with the policy features
* remove double normalization overwrite
* cleanup from pretrained
* remove typo
* also set norm_map
* fix(augment_quantiles) images incorrectly divided by 255
* clamp quantiles
* link to lerobot base models
* rename tests
* encorperate PR feedback
* update docstring for RunningQuantileStats
* update doc links
* Revert "clamp quantiles"
This reverts commit 172207471c.
* fix self.paligemma
* fix tests related to quantiles that were scaled to [0,1], the new range is [-1, 1]
* fix libero doc and use different transformer branch
* use fix branch instead of feat
* update results libero
* add new line
* fix formatting
* precommit
* update results libero
* update libero doc
* update title
* final changes
* add quantiles to test
* run pre commit
---------
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
Revert "feat(normalization): add validation for empty features in NormalizerProcessorStep and UnnormalizerProcessorStep (#2087)"
This reverts commit f173265354.
* chore: replace hard-coded 'action' values with constants throughout all the source code
* chore(tests): replace hard-coded action values with constants throughout all the test code
* chore: replace hard-coded OBS values with constants throughout all the source code
* chore(tests): replace hard-coded OBS values with constants throughout all the test code
Steven Palma